{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 一、数据分析和处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3327, 81) (1427, 81) (3327,) (1427,)\n",
      "0    1068\n",
      "1     359\n",
      "Name: status, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df = pd.read_csv('data.csv',encoding = 'gbk')\n",
    "\n",
    "\"\"\"\n",
    "数据处理\n",
    "\"\"\"\n",
    "\n",
    "###删除无关特征###\n",
    "df.drop(['Unnamed: 0','custid','trade_no','bank_card_no' ,'source','id_name'],inplace =True,axis = 1)\n",
    "\n",
    "###数据类型转换###（主要针对 obeject（文字类） \n",
    "df['reg_preference_for_trad'].fillna('其他城市',inplace = True)\n",
    "df['reg_preference_for_trad'].replace({'一线城市':1,'二线城市':2,'三线城市':3,'境外':4,'其他城市':5},inplace = True)\n",
    "\n",
    "# 处理日期格式 'latest_query_time', 'loans_latest_time'(暂时去掉？？？)\n",
    "\n",
    "df.drop(['latest_query_time', 'loans_latest_time'],inplace =True,axis = 1)\n",
    "\n",
    "\n",
    "###缺失值处理###\n",
    "df['student_feature'].fillna(0,inplace=True) \n",
    "for i in df.columns:\n",
    "    df[i].fillna(df[i].mode()[0],inplace = True)  #加[0]是因为众数可能有多个，返回不是一个数字\n",
    "    \n",
    "###切分数据集###\n",
    "\n",
    "y=df['status']\n",
    "x=df.drop('status',axis=1)\n",
    "x_train,x_test,y_train,y_test =train_test_split(x,y,test_size=0.3,random_state = 2018)   \n",
    "print(x_train.shape, x_test.shape, y_train.shape, y_test.shape)\n",
    "print(y_test.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 二、特征选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 三、建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import BaggingClassifier, RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.linear_model import Perceptron\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgb\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from pandas import DataFrame,Series\n",
    "\n",
    "### 标准化处理 ### 必须要做的\n",
    "features = x_train.columns #目前是83个特征\n",
    "scaler = StandardScaler()\n",
    "x_train = scaler.fit_transform(x_train)\n",
    "x_test = scaler.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.基本的分类器 ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Perceptron(alpha=0.0001, class_weight=None, eta0=1.0, fit_intercept=True,\n",
       "      max_iter=50, n_iter=None, n_jobs=1, penalty=None, random_state=0,\n",
       "      shuffle=True, tol=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#感知机\n",
    "clf_pt = Perceptron(max_iter=50)\n",
    "clf_pt.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#逻辑回归\n",
    "clf_lr = LogisticRegression()\n",
    "clf_lr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#svm\n",
    "clf_svm = SVC(gamma='auto',probability=True)\n",
    "clf_svm.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#决策树\n",
    "clf_dt = DecisionTreeClassifier()\n",
    "clf_dt.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 高斯朴素贝叶斯\n",
    "clf_gnb = GaussianNB()\n",
    "clf_gnb.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.基于sklearn的集成学习 ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=-1,\n",
       "            oob_score=False, random_state=2018, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#随机森林\n",
    "clf_rf = RandomForestClassifier(random_state=2018, n_jobs=-1)\n",
    "clf_rf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=1, min_samples_split=2,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "              presort='auto', random_state=None, subsample=1.0, verbose=0,\n",
       "              warm_start=False)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GBDT\n",
    "clf_gbdt = GradientBoostingClassifier()\n",
    "clf_gbdt.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  1.5min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "BaggingClassifier(base_estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "         bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
       "         max_samples=1.0, n_estimators=60, n_jobs=1, oob_score=False,\n",
       "         random_state=2018, verbose=1, warm_start=False)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Bagging 这个耗时长\n",
    "clf_bg = BaggingClassifier(base_estimator=SVC(gamma='auto',probability=True), n_estimators=60, max_samples=1.0, max_features=1.0, random_state=2018,\n",
    "                        n_jobs=1, verbose=1)\n",
    "clf_bg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(algorithm='SAMME',\n",
       "          base_estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "          learning_rate=1.0, n_estimators=50, random_state=None)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " #Adaboost 这个耗时最长\n",
    "clf_ab = AdaBoostClassifier(base_estimator=SVC(gamma='auto',probability=True), n_estimators=50,algorithm='SAMME')\n",
    "clf_ab.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.大杀器 ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=6, min_child_weight=3, missing=None, n_estimators=100,\n",
       "       n_jobs=1, nthread=32, num_class=2, objective='multi:softmax',\n",
       "       random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
       "       seed=1000, silent=True, subsample=1)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#xgb\n",
    "clf_xgb = xgb.XGBClassifier(max_depth=6, num_class =2,learning_rate=0.1, n_estimators=100, silent=True, objective='multi:softmax',\n",
    "                        nthread=32, gamma=0.1, min_child_weight=3, max_delta_step=0, subsample=1, colsample_bytree=1,\n",
    "                        colsample_bylevel=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, seed=1000,\n",
    "                        missing=None)\n",
    "clf_xgb.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
       "        importance_type='split', learning_rate=0.1, max_bin=255,\n",
       "        max_depth=-1, min_child_samples=20, min_child_weight=0.001,\n",
       "        min_split_gain=0.0, n_estimators=250, n_jobs=-1, num_leaves=31,\n",
       "        objective=None, random_state=None, reg_alpha=0.0, reg_lambda=0.5,\n",
       "        silent=True, subsample=1.0, subsample_for_bin=200000,\n",
       "        subsample_freq=1)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lgbm\n",
    "clf_lgbm = lgb.LGBMClassifier(boosting_type='gbdt', num_leaves=31, max_depth=-1, learning_rate=0.1, n_estimators=250,\n",
    "                              max_bin=255, subsample_for_bin=200000, objective=None, min_split_gain=0.0, min_child_weight=0.001,\n",
    "                              min_child_samples=20, subsample=1.0, subsample_freq=1, colsample_bytree=1.0, reg_alpha=0.0,\n",
    "                              reg_lambda=0.5, random_state=None, n_jobs=-1, silent=True)\n",
    "clf_lgbm.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   21.4s finished\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    9.1s finished\n"
     ]
    }
   ],
   "source": [
    "# 11个模型：\n",
    "##感知机,逻辑回归,svm,决策树,高斯朴素贝叶斯，随机森林，GBDT，Bagging ，Adaboost，xgb，lgbm\n",
    "\n",
    "#对train\n",
    "y_train_pt = clf_pt.predict(x_train)\n",
    "y_train_lr = clf_lr.predict(x_train)\n",
    "y_train_svm = clf_svm.predict(x_train)\n",
    "y_train_dt = clf_dt.predict(x_train)\n",
    "y_train_gnb = clf_gnb.predict(x_train)\n",
    "\n",
    "y_train_rf = clf_rf.predict(x_train)\n",
    "y_train_gbdt = clf_gbdt.predict(x_train)\n",
    "y_train_bg = clf_bg.predict(x_train)\n",
    "y_train_ab = clf_ab.predict(x_train)\n",
    "\n",
    "y_train_xgb = clf_xgb.predict(x_train)\n",
    "y_train_lgbm = clf_lgbm.predict(x_train)\n",
    "\n",
    "#对test\n",
    "y_test_pt = clf_pt.predict(x_test)\n",
    "y_test_lr = clf_lr.predict(x_test)\n",
    "y_test_svm = clf_svm.predict(x_test)\n",
    "y_test_dt = clf_dt.predict(x_test)\n",
    "y_test_gnb = clf_gnb.predict(x_test)\n",
    "\n",
    "y_test_rf = clf_rf.predict(x_test)\n",
    "y_test_gbdt = clf_gbdt.predict(x_test)\n",
    "y_test_bg = clf_bg.predict(x_test)\n",
    "y_test_ab = clf_ab.predict(x_test)\n",
    "\n",
    "y_test_xgb = clf_xgb.predict(x_test)\n",
    "y_test_lgbm = clf_lgbm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   21.3s finished\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    9.0s finished\n"
     ]
    }
   ],
   "source": [
    "#概率值\n",
    "#对train\n",
    "# y_train_pro_pt = clf_pt.predict_proba(x_train) # Perceptron没有predict_proba\n",
    "y_train_pro_lr = clf_lr.predict_proba(x_train)\n",
    "y_train_pro_svm = clf_svm.predict_proba(x_train)\n",
    "y_train_pro_dt = clf_dt.predict_proba(x_train)\n",
    "y_train_pro_gnb = clf_gnb.predict_proba(x_train)\n",
    "\n",
    "y_train_pro_rf = clf_rf.predict_proba(x_train)\n",
    "y_train_pro_gbdt = clf_gbdt.predict_proba(x_train)\n",
    "y_train_pro_bg = clf_bg.predict_proba(x_train)\n",
    "y_train_pro_ab = clf_ab.predict_proba(x_train)\n",
    "\n",
    "y_train_pro_xgb = clf_xgb.predict_proba(x_train)\n",
    "y_train_pro_lgbm = clf_lgbm.predict_proba(x_train)\n",
    "\n",
    "#对test\n",
    "# y_test_pro_pt = clf_pt.predict_proba(x_test)\n",
    "y_test_pro_lr = clf_lr.predict_proba(x_test)\n",
    "y_test_pro_svm = clf_svm.predict_proba(x_test)\n",
    "y_test_pro_dt = clf_dt.predict_proba(x_test)\n",
    "y_test_pro_gnb = clf_gnb.predict_proba(x_test)\n",
    "\n",
    "y_test_pro_rf = clf_rf.predict_proba(x_test)\n",
    "y_test_pro_gbdt = clf_gbdt.predict_proba(x_test)\n",
    "y_test_pro_bg = clf_bg.predict_proba(x_test)\n",
    "y_test_pro_ab = clf_ab.predict_proba(x_test)\n",
    "\n",
    "y_test_pro_xgb = clf_xgb.predict_proba(x_test)\n",
    "y_test_pro_lgbm = clf_lgbm.predict_proba(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 四、模型评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 混淆矩阵\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix, precision_score, accuracy_score,recall_score, f1_score,roc_auc_score\n",
    "\n",
    "def get_eval(y_train,y_test,y_train_pred,y_test_pred,model_name):\n",
    "    train_dataList = []\n",
    "    test_dataList = []\n",
    "    print('for model {}:'.format(str(model_name)))\n",
    "    train_dataList.append(str(model_name))\n",
    "    test_dataList.append(str(model_name))\n",
    "    train_dataList.append(str('train'))\n",
    "    test_dataList.append(str('test'))\n",
    "\n",
    "#   混淆矩阵\n",
    "    obj1 = confusion_matrix(y_train, y_train_pred)\n",
    "    print('train_confusion_matrix\\n', obj1)\n",
    "    obj2 = confusion_matrix(y_test, y_test_pred)\n",
    "    print('test_confusion_matrix\\n', obj2)  \n",
    "    ans = classification_report(y_train,y_train_pred,digits=5)\n",
    "    print('综合评价:\\n',ans)\n",
    "#     print('accuracy:\\n')\n",
    "#     print('train:{}\\n test:{}\\n'.format(accuracy_score(y_train,y_train_pred),accuracy_score(y_test,y_test_pred)))\n",
    "    train_dataList.append(accuracy_score(y_train,y_train_pred))\n",
    "    test_dataList.append(accuracy_score(y_test,y_test_pred))\n",
    "#     print('precision:\\n')\n",
    "#     print('train:{}\\n test:{}\\n'.format(precision_score(y_train,y_train_pred),precision_score(y_test,y_test_pred)))\n",
    "    train_dataList.append(precision_score(y_train,y_train_pred))\n",
    "    test_dataList.append(precision_score(y_test,y_test_pred))\n",
    "#     print('recall:\\n')\n",
    "#     print('train:{}\\n test:{}\\n'.format(recall_score(y_train,y_train_pred),recall_score(y_test,y_test_pred)))\n",
    "    train_dataList.append(recall_score(y_train,y_train_pred))\n",
    "    test_dataList.append(recall_score(y_test,y_test_pred))\n",
    "#     print('f1-score:\\n')\n",
    "#     print('train:{}\\n test:{}\\n'.format(f1_score(y_train,y_train_pred),f1_score(y_test,y_test_pred)))\n",
    "    train_dataList.append(f1_score(y_train,y_train_pred))\n",
    "    test_dataList.append(f1_score(y_test,y_test_pred))\n",
    "#     print('AUC:\\n')\n",
    "#     print('train:{}\\n test:{}\\n'.format(roc_auc_score(y_train,y_train_pred),roc_auc_score(y_test,y_test_pred)))\n",
    "    train_dataList.append(roc_auc_score(y_train,y_train_pred))\n",
    "    test_dataList.append(roc_auc_score(y_test,y_test_pred))\n",
    "    return train_dataList,test_dataList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for model 感知机:\n",
      "train_confusion_matrix\n",
      " [[2131  362]\n",
      " [ 472  362]]\n",
      "test_confusion_matrix\n",
      " [[874 194]\n",
      " [212 147]]\n",
      "综合评价:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0    0.81867   0.85479   0.83634      2493\n",
      "          1    0.50000   0.43405   0.46470       834\n",
      "\n",
      "avg / total    0.73879   0.74932   0.74318      3327\n",
      "\n",
      "for model 逻辑回归:\n",
      "train_confusion_matrix\n",
      " [[2361  132]\n",
      " [ 519  315]]\n",
      "test_confusion_matrix\n",
      " [[998  70]\n",
      " [234 125]]\n",
      "综合评价:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0    0.81979   0.94705   0.87884      2493\n",
      "          1    0.70470   0.37770   0.49180       834\n",
      "\n",
      "avg / total    0.79094   0.80433   0.78182      3327\n",
      "\n",
      "for model SVM:\n",
      "train_confusion_matrix\n",
      " [[2460   33]\n",
      " [ 495  339]]\n",
      "test_confusion_matrix\n",
      " [[1028   40]\n",
      " [ 275   84]]\n",
      "综合评价:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0    0.83249   0.98676   0.90308      2493\n",
      "          1    0.91129   0.40647   0.56219       834\n",
      "\n",
      "avg / total    0.85224   0.84130   0.81763      3327\n",
      "\n",
      "for model 决策树:\n",
      "train_confusion_matrix\n",
      " [[2493    0]\n",
      " [   0  834]]\n",
      "test_confusion_matrix\n",
      " [[799 269]\n",
      " [198 161]]\n",
      "综合评价:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0    1.00000   1.00000   1.00000      2493\n",
      "          1    1.00000   1.00000   1.00000       834\n",
      "\n",
      "avg / total    1.00000   1.00000   1.00000      3327\n",
      "\n",
      "for model 高斯朴素贝叶斯:\n",
      "train_confusion_matrix\n",
      " [[1401 1092]\n",
      " [ 196  638]]\n",
      "test_confusion_matrix\n",
      " [[952 116]\n",
      " [264  95]]\n",
      "综合评价:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0    0.87727   0.56197   0.68509      2493\n",
      "          1    0.36879   0.76499   0.49766       834\n",
      "\n",
      "avg / total    0.74981   0.61286   0.63810      3327\n",
      "\n",
      "for model 随机森林:\n",
      "train_confusion_matrix\n",
      " [[2493    0]\n",
      " [  50  784]]\n",
      "test_confusion_matrix\n",
      " [[992  76]\n",
      " [278  81]]\n",
      "综合评价:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0    0.98034   1.00000   0.99007      2493\n",
      "          1    1.00000   0.94005   0.96910       834\n",
      "\n",
      "avg / total    0.98527   0.98497   0.98481      3327\n",
      "\n",
      "for model GBDT:\n",
      "train_confusion_matrix\n",
      " [[2432   61]\n",
      " [ 405  429]]\n",
      "test_confusion_matrix\n",
      " [[997  71]\n",
      " [256 103]]\n",
      "综合评价:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0    0.85724   0.97553   0.91257      2493\n",
      "          1    0.87551   0.51439   0.64804       834\n",
      "\n",
      "avg / total    0.86182   0.85993   0.84626      3327\n",
      "\n",
      "for model Bagging:\n",
      "train_confusion_matrix\n",
      " [[2450   43]\n",
      " [ 476  358]]\n",
      "test_confusion_matrix\n",
      " [[1022   46]\n",
      " [ 269   90]]\n",
      "综合评价:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0    0.83732   0.98275   0.90423      2493\n",
      "          1    0.89277   0.42926   0.57976       834\n",
      "\n",
      "avg / total    0.85122   0.84400   0.82289      3327\n",
      "\n",
      "for model AdaBoosting:\n",
      "train_confusion_matrix\n",
      " [[2493    0]\n",
      " [ 834    0]]\n",
      "test_confusion_matrix\n",
      " [[1068    0]\n",
      " [ 359    0]]\n",
      "综合评价:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0    0.74932   1.00000   0.85670      2493\n",
      "          1    0.00000   0.00000   0.00000       834\n",
      "\n",
      "avg / total    0.56149   0.74932   0.64195      3327\n",
      "\n",
      "for model XGBoosting:\n",
      "train_confusion_matrix\n",
      " [[2492    1]\n",
      " [  41  793]]\n",
      "test_confusion_matrix\n",
      " [[1008   60]\n",
      " [ 261   98]]\n",
      "综合评价:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0    0.98381   0.99960   0.99164      2493\n",
      "          1    0.99874   0.95084   0.97420       834\n",
      "\n",
      "avg / total    0.98756   0.98738   0.98727      3327\n",
      "\n",
      "for model lightGBM:\n",
      "train_confusion_matrix\n",
      " [[2493    0]\n",
      " [   0  834]]\n",
      "test_confusion_matrix\n",
      " [[989  79]\n",
      " [252 107]]\n",
      "综合评价:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0    1.00000   1.00000   1.00000      2493\n",
      "          1    1.00000   1.00000   1.00000       834\n",
      "\n",
      "avg / total    1.00000   1.00000   1.00000      3327\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\MyCodeEnvironment\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "D:\\MyCodeEnvironment\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "D:\\MyCodeEnvironment\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "all_dataList = []\n",
    "index = []\n",
    "a1,a2 = get_eval(y_train,y_test,y_train_pt,y_test_pt,'感知机')\n",
    "b1,b2 = get_eval(y_train,y_test,y_train_lr,y_test_lr,'逻辑回归')\n",
    "c1,c2 = get_eval(y_train,y_test,y_train_svm,y_test_svm,'SVM')\n",
    "d1,d2 = get_eval(y_train,y_test,y_train_dt,y_test_dt,'决策树')\n",
    "e1,e2 = get_eval(y_train,y_test,y_train_gnb,y_test_gnb,'高斯朴素贝叶斯')\n",
    "f1,f2 = get_eval(y_train,y_test,y_train_rf,y_test_rf,'随机森林')\n",
    "g1,g2 = get_eval(y_train,y_test,y_train_gbdt,y_test_gbdt,'GBDT')\n",
    "h1,h2 = get_eval(y_train,y_test,y_train_bg,y_test_bg,'Bagging')\n",
    "i1,i2 = get_eval(y_train,y_test,y_train_ab,y_test_ab,'AdaBoosting')\n",
    "j1,j2 = get_eval(y_train,y_test,y_train_xgb,y_test_xgb,'XGBoosting')\n",
    "k1,k2 = get_eval(y_train,y_test,y_train_lgbm,y_test_lgbm,'lightGBM')\n",
    "all_dataList = [a1,a2,b1,b2,c1,c2,d1,d2,e1,e2,f1,f2,g1,g2,h1,h2,i1,i2,j1,j2,k1,k2]\n",
    "# all_dataList = [a1,a2,b1,b2,c1,c2,d1,d2,e1,e2,f1,f2,g1,g2,j1,j2,k1,k2]\n",
    "df_all = pd.DataFrame(all_dataList,columns=['model_name','dataset','accuracy','precision','recall','f1_score','AUC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_name</th>\n",
       "      <th>dataset</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1_score</th>\n",
       "      <th>AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>感知机</td>\n",
       "      <td>train</td>\n",
       "      <td>0.749324</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.434053</td>\n",
       "      <td>0.464698</td>\n",
       "      <td>0.644423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>感知机</td>\n",
       "      <td>test</td>\n",
       "      <td>0.715487</td>\n",
       "      <td>0.431085</td>\n",
       "      <td>0.409471</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.613911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>逻辑回归</td>\n",
       "      <td>train</td>\n",
       "      <td>0.804328</td>\n",
       "      <td>0.704698</td>\n",
       "      <td>0.377698</td>\n",
       "      <td>0.491803</td>\n",
       "      <td>0.662375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>逻辑回归</td>\n",
       "      <td>test</td>\n",
       "      <td>0.786966</td>\n",
       "      <td>0.641026</td>\n",
       "      <td>0.348189</td>\n",
       "      <td>0.451264</td>\n",
       "      <td>0.641323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVM</td>\n",
       "      <td>train</td>\n",
       "      <td>0.841298</td>\n",
       "      <td>0.911290</td>\n",
       "      <td>0.406475</td>\n",
       "      <td>0.562189</td>\n",
       "      <td>0.696619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SVM</td>\n",
       "      <td>test</td>\n",
       "      <td>0.779257</td>\n",
       "      <td>0.677419</td>\n",
       "      <td>0.233983</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>0.598265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>决策树</td>\n",
       "      <td>train</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>决策树</td>\n",
       "      <td>test</td>\n",
       "      <td>0.672740</td>\n",
       "      <td>0.374419</td>\n",
       "      <td>0.448468</td>\n",
       "      <td>0.408112</td>\n",
       "      <td>0.598298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>高斯朴素贝叶斯</td>\n",
       "      <td>train</td>\n",
       "      <td>0.612864</td>\n",
       "      <td>0.368786</td>\n",
       "      <td>0.764988</td>\n",
       "      <td>0.497660</td>\n",
       "      <td>0.663481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>高斯朴素贝叶斯</td>\n",
       "      <td>test</td>\n",
       "      <td>0.733707</td>\n",
       "      <td>0.450237</td>\n",
       "      <td>0.264624</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.578005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>随机森林</td>\n",
       "      <td>train</td>\n",
       "      <td>0.984971</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.940048</td>\n",
       "      <td>0.969098</td>\n",
       "      <td>0.970024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>随机森林</td>\n",
       "      <td>test</td>\n",
       "      <td>0.751927</td>\n",
       "      <td>0.515924</td>\n",
       "      <td>0.225627</td>\n",
       "      <td>0.313953</td>\n",
       "      <td>0.577233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>train</td>\n",
       "      <td>0.859934</td>\n",
       "      <td>0.875510</td>\n",
       "      <td>0.514388</td>\n",
       "      <td>0.648036</td>\n",
       "      <td>0.744960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>test</td>\n",
       "      <td>0.770848</td>\n",
       "      <td>0.591954</td>\n",
       "      <td>0.286908</td>\n",
       "      <td>0.386492</td>\n",
       "      <td>0.610214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Bagging</td>\n",
       "      <td>train</td>\n",
       "      <td>0.844004</td>\n",
       "      <td>0.892768</td>\n",
       "      <td>0.429257</td>\n",
       "      <td>0.579757</td>\n",
       "      <td>0.706004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Bagging</td>\n",
       "      <td>test</td>\n",
       "      <td>0.779257</td>\n",
       "      <td>0.661765</td>\n",
       "      <td>0.250696</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.603813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AdaBoosting</td>\n",
       "      <td>train</td>\n",
       "      <td>0.749324</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>AdaBoosting</td>\n",
       "      <td>test</td>\n",
       "      <td>0.748423</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>XGBoosting</td>\n",
       "      <td>train</td>\n",
       "      <td>0.987376</td>\n",
       "      <td>0.998741</td>\n",
       "      <td>0.950839</td>\n",
       "      <td>0.974201</td>\n",
       "      <td>0.975219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>XGBoosting</td>\n",
       "      <td>test</td>\n",
       "      <td>0.775053</td>\n",
       "      <td>0.620253</td>\n",
       "      <td>0.272981</td>\n",
       "      <td>0.379110</td>\n",
       "      <td>0.608400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>lightGBM</td>\n",
       "      <td>train</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>lightGBM</td>\n",
       "      <td>test</td>\n",
       "      <td>0.768045</td>\n",
       "      <td>0.575269</td>\n",
       "      <td>0.298050</td>\n",
       "      <td>0.392661</td>\n",
       "      <td>0.612040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     model_name dataset  accuracy  precision    recall  f1_score       AUC\n",
       "0           感知机   train  0.749324   0.500000  0.434053  0.464698  0.644423\n",
       "1           感知机    test  0.715487   0.431085  0.409471  0.420000  0.613911\n",
       "2          逻辑回归   train  0.804328   0.704698  0.377698  0.491803  0.662375\n",
       "3          逻辑回归    test  0.786966   0.641026  0.348189  0.451264  0.641323\n",
       "4           SVM   train  0.841298   0.911290  0.406475  0.562189  0.696619\n",
       "5           SVM    test  0.779257   0.677419  0.233983  0.347826  0.598265\n",
       "6           决策树   train  1.000000   1.000000  1.000000  1.000000  1.000000\n",
       "7           决策树    test  0.672740   0.374419  0.448468  0.408112  0.598298\n",
       "8       高斯朴素贝叶斯   train  0.612864   0.368786  0.764988  0.497660  0.663481\n",
       "9       高斯朴素贝叶斯    test  0.733707   0.450237  0.264624  0.333333  0.578005\n",
       "10         随机森林   train  0.984971   1.000000  0.940048  0.969098  0.970024\n",
       "11         随机森林    test  0.751927   0.515924  0.225627  0.313953  0.577233\n",
       "12         GBDT   train  0.859934   0.875510  0.514388  0.648036  0.744960\n",
       "13         GBDT    test  0.770848   0.591954  0.286908  0.386492  0.610214\n",
       "14      Bagging   train  0.844004   0.892768  0.429257  0.579757  0.706004\n",
       "15      Bagging    test  0.779257   0.661765  0.250696  0.363636  0.603813\n",
       "16  AdaBoosting   train  0.749324   0.000000  0.000000  0.000000  0.500000\n",
       "17  AdaBoosting    test  0.748423   0.000000  0.000000  0.000000  0.500000\n",
       "18   XGBoosting   train  0.987376   0.998741  0.950839  0.974201  0.975219\n",
       "19   XGBoosting    test  0.775053   0.620253  0.272981  0.379110  0.608400\n",
       "20     lightGBM   train  1.000000   1.000000  1.000000  1.000000  1.000000\n",
       "21     lightGBM    test  0.768045   0.575269  0.298050  0.392661  0.612040"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 同时看train和test可以知道有没有过拟合\n",
    "df_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 画ROC曲线\n",
    "from sklearn.metrics import roc_curve\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "def draw_roc(y_true, y_pred,label=None):\n",
    "    fpr, tpr, thresholds = roc_curve(y_true, y_pred)\n",
    "    plt.plot(fpr, tpr, linewidth = 2, label=label)\n",
    "    plt.plot([0,1],[0,1], 'k--')\n",
    "    plt.axis([-0.05,1.05,-0.05,1.05])\n",
    "    plt.xlabel('False Postivie Rate')\n",
    "    plt.ylabel('True Positive Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bmcBXkXGcyTB+0NV2ceD+rg2ZUmcH9fe9CDEnjZ3dGhitq4Mmg2vdMp3nyJEjDBo0iLi4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\n5NwcNdxMrMScu5VSco6IFZLmAEuBTcDUiGj1p42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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c58347cc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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psPcE7qwGZbQSitY2slaC477//nt69+5dWMBu7dq1ZGRkcPrpp7sdmjGeVlpS\nOKKqBwFUdU8ZxxanC76FbptU9SjwJtCnmONGADOBiNquKrDLaMv4XsW3El6/Gsae9MtjSwZh8eij\nj9KkSRM++OADnn/et5V469atXY7KmMhQ2pjCaQF7MwtweuBezap6ZRmf3RjYHvA4Ezg38AARaQz0\nw7d3Q+dgg3Zb0AvVrMsorDZu3EhycjIZGRlUrVqV5557jltvLVcD15iYV1pS6F/k8fMOnP9p4B5V\nzReREg8SkaHAUICmTZs6EEb5BD2wXGDsfocjMgDnnHMO+/fv54ILLuCDDz6wrTGNqYDS9mj+5AQ/\newe+DXoKNPE/FygJeNOfEBKBVBHJU9X/FollMjAZICkpSXFRUK0EW5wWNqtXr6Zhw4Y0aNCAp556\nipo1a3LNNde4HZYxESuYxWsVtQxo6a+qugMYCPwh8ABVbVHwu4i8DLxfNCF4TamthOJmGdk4giPy\n8/MZOnQoU6dOLSxgd9NNN7kdljERz7GkoKp5IjIcmAdUAqaq6loRucX/+iSnzu2EorONim0lWEII\niy+++IK+ffuyb98+6tSpw8MPP+x2SMZEjaCTgohUU9Uj5flwVU0D0oo8V2wyUNUby/PZ4VTm9NOi\nLQQbQ3DMPffcw+OP+9ZRXn311cyYMYPKlZ1s8BoTW8qcZioiXUTkG2CD/3EHEXnO8cg8pMzppzbL\nyHEFBewuvfRSGjZsyBdffMFbb71lCcGYEAvm/1HPAr3xrW5GVb8WkUscjcqjSlyLUMBaCCF36NAh\n+vbty/r169myZQs9e/Zk165dbodlTNQKZkFanKpuLfLcMSeC8aIyi93ZLCPHvP766yQmJvLRRx+R\nn5/PgQMH3A7JmKgXTFLYLiJdABWRSiIyEljvcFyeUeZsowI2qBwyWVlZnH/++QwePJgjR44watQo\ntm3bRp06ddwOzZioF0z30a34upCaAj8AH1POOkiRqtQ1CbYWwTF79uxhyZIltGzZknnz5tGiRYuy\n32SMCYkyWwqqultVB6pqov9noKruDUdwbiu1lWD7IoTUzp07SU1N5ejRo7Rq1Yr09HTWr19vCcGY\nMAtm9tFLIjK56E84gnNT0PWNLCGcsHHjxtG0aVPmzJnDxIkTAWjVqpXLURkTm4LpPvo44Pfq+ArY\nbS/h2KgQuC6h1PpG5oRs2LCB5ORkNm3aRNWqVZk4cSJDhw51OyxjYlqZSUFV/x34WEReBb5wLCIP\nCEwIZU714JrWAAASe0lEQVRDNRXWuXNn9u/fz0UXXcT7779vA8nGeEBFVv60AH4T6kC8IqhuIxtg\nrrBVq1bRsGFDGjZsyDPPPEONGjUYMGCA22EZY/zKTAoi8iNQUJk0DsgCStxaM9KVq+CdjScELT8/\nnyFDhvDyyy/TqVMnvvrqK2644Qa3wzLGFFFqUhBfTesO/FLyOl9VXS1dHS5BFbwzQZk/fz79+/cn\nKyuLk046iX/84x9uh2SMKUGpSUFVVUTSVLVduAJyU5mrlwtYOYug3X333UyYMAGAa6+9lunTp1u9\nImM8LJgVzatE5GzHI/GAoFcvmzIVFLDr0aMHjRo1YvHixVbR1JgIUOL/Q0WksqrmAWcDy0RkI3AQ\n337NqqqdwhRj2JXadWTdRqXKycmhb9++bNiwgc2bN9OjRw927Ci64Z4xxqtK+9q2FOgEXBGmWCKD\nDS6XaPr06QwbNozc3FyaNm1KTk6OTTM1JsKUlhQEQFU3hikWE6GysrJITk5m2bJlxMXFcd999/HI\nI4+4HZYxpgJKSwr1ReSOkl5U1ScdiMdEoD179rB8+XLOPPNM5s6dS7NmzdwOyRhTQaUNNFcC4oHa\nJfxElaBnHhkAMjMzSU5OLixgt27dOtLT0y0hGBPhSmsp7FLVh8IWiYus1lH5jB07lnHjxnHs2DEm\nTpzIyJEjadmypdthGWNCoMwxhWhXNCFYraOSpaenk5KSwtatW6lWrRovvvgiQ4YMcTssY0wIlZYU\nuoctChcFlRBsOioA5513HgcOHKBbt2689957xMfHux2SMSbESkwKqpoVzkDcUGbxu6IJIQanoy5f\nvpwmTZrQsGFD/vWvf1GrVi369evndljGGIfE9PLSMscRYjgh5Ofnc+ONN/Lqq68WFrAbPHiw22EZ\nYxwW00mhQJnjCDGWED799FP69+9PdnY2devWLaxdZIyJfsHUPopNMTqOcOedd9K9e3eys7O57rrr\n2LdvH5deeqnbYRljwiRmk0LQ6xJipJVQUMAuOTmZxo0bs3TpUqZPn05cXMz+J2JMTIrZ7iNbl+CT\nk5PD5ZdfzsaNG9myZQs9evQgMzPT7bCMMS6J+a+BJW65GQOmTZtGYmIi8+fPJy4ujpycHLdDMsa4\nLOaTQizau3cvSUlJ3Hzzzfz888+MGTOGLVu2WEVTY0zsdh+VqOg+zFHoxx9/ZOXKlbRp04Z58+bR\npEkTt0MyxniEoy0FEUkWkXUikiEio4p5fZCIrBaRb0RkoYh0cDKeoETpPszbtm2jR48e5Obm0rJl\nSzIyMli7dq0lBGPMrziWFESkEvAvIAVoA1wrIm2KHLYZ+J2qtgceBiY7FU+goGYejd0fNTOPxowZ\nQ4sWLfj444+ZNGkSAC1atHA5KmOMFznZfdQFyFDVTQAi8ibQB/i24ABVXRhw/GIgLF9bS5x5FGWF\n79LT00lOTmbbtm1Ur16dF198keuvv97tsIwxHuZkUmgMbA94nAmcW8rxQ4A5xb0gIkOBoQBNmzYN\nVXzHzzyKsgVrBQXsLr30UmbNmmUF7IwxZfLEQLOIXIIvKVxY3OuqOhl/11JSUpI6HlAEdxstW7aM\nU089lYYNGzJx4kTi4+Pp06eP22EZYyKEk0lhB3BqwOMm/ud+RUTOAqYAKaq6z8F4olpeXh433HAD\nM2bM4Oyzz2bFihUMGjTI7bCMMRHGydlHy4CWItJCRKoCA4HZgQeISFPgHeA6VV3vYCxR7aOPPiIx\nMZEZM2Zw8skn8+STtn22MaZiHEsKqpoHDAfmAenAW6q6VkRuEZFb/Ic9ANQDJorIKhFZ7lQ80eqO\nO+7gsssuY//+/dx4443s3buXbt26uR2WMSZCOTqmoKppQFqR5yYF/P5H4I9OxhC0CJt5lJ+fT1xc\nHL169eLtt9/mv//9L506dXI7LGNMhPPEQLMnRMjMowMHDtC7d282bdrEtm3b6N69O9u2bXM7LGNM\nlLDaR0V5eObRlClTaNCgAQsWLKBatWpWwM4YE3KWFMDzXUe7d++mU6dO/OlPfyIvL48HH3yQjRs3\nWgE7Y0zIWfcReL7raP/+/Xz99de0b9+euXPn0qhRI7dDMsZEKWspBPJQ19HWrVvp3r17YQG7TZs2\nsXr1aksIxhhHWVLwYNfRvffey2mnncann37KSy+9BECzZs1cjsoYEwus+8hDXUdr1qwhJSWFzMxM\nqlevzksvvcTgwYPdDssYE0NiLimUWDbbA11HXbt25cCBA/z+979n1qxZ1KxZ0+2QjDExJuaSQoll\ns12yaNEimjVrRqNGjZg0aRK1atXiiiuucDssY0yMirmkUOC4stlhlpeXxx/+8Af+85//FBawu/ba\na12NyRhjYjYpuGnOnDkMHDiQAwcOkJCQwLPPPut2SMYYA8T67CMXZh799a9/JTU1lZ9++okhQ4aw\nZ88eLryw2G0kjDEm7GI7KYRx5lF+fj4Al19+Oc2aNWPVqlVMmTKFuLjY/icwxnhL7N6RAlsJDs48\nys7OpmvXrpx66qnk5+dz6aWXsmXLFs466yzHzmmMMRUVU0nhV9NRw9BKePHFF/nNb37DwoULqVGj\nhhWwM8Z4XkwlhYLpqLNOfvqXJx1oJXz//fd06NCBW265hWPHjjFu3DgyMjKsgJ0xxvNibvbR1CqP\n0+HwKt8Dh1oJBw8eZM2aNVbAzhgTcWKqpQBwaaWAhBDCVsLGjRvp1q0bubm5nH766WzevNkK2Blj\nIk7MJYVCIUwId911F2eccQb/+9//CgvYNW3aNGSfb4wx4RJT3UdTqzwe0s9btWoVvXr1YufOndSo\nUYNp06ZxzTXXhPQcxhgTTjGVFH7VdRQCv/vd7zhw4ADJycm8++67VK9ePSSfa4wxbomppFDoBLqO\nvvzyS1q0aEGjRo2YPHkyderUISUlJYTBGWOMe2IzKVRAXl4eAwcOZObMmXTs2JGVK1daV5ExJurE\n7kBzOaSlpZGQkMDMmTNJTEzk+eefdzskY4xxhCWFMtx+++306tWLnJwchg0bxg8//EDXrl3dDssY\nYxwRO0mhnBVR8/LyAOjbty/Nmzdn9erVTJo0yQrYGWOiWuzc4fy1jj491rHUw7KysjjvvPNo2rRp\nYQG7zZs3065du3BEaYwxroqdpOD36mkTSnzt+eefp2HDhixZsoTatWtz6NChMEZmjDHui7mkUNw2\nnDt37qR9+/aMGDECVeWxxx5j3bp1xMfHuxChMca4x6akAocPH+bbb7/l7LPPZu7cuTRo0MDtkIwx\nxhWOthREJFlE1olIhoiMKuZ1EZFn/a+vFpFOTsYTaMOGDVx88cWFBey2b9/OihUrLCEYY2KaY0lB\nRCoB/wJSgDbAtSLSpshhKUBL/89Q4AWn4imQn5/PHXfcQatWrViwYAFTpkwBsGqmxhiDs91HXYAM\nVd0EICJvAn2AbwOO6QNMV1UFFotIXRE5RVV3ORHQzp/ySWrShF27dlGzZk2mTZvGgAEDnDiVMcZE\nJCeTQmNge8DjTODcII5pDDiSFAb85zC7duWQmprKzJkzrYCdMcYUEREDzSIyFF/3UoX3KWieO4OD\nnRYw96VL6NmzZyjDM8aYqOF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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c5ec6696d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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boymGk1Ew8+qLt7fopwnBy6xbt45bbrmFo0ePEhISwoQJE+wOSZViziQFP2PM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c5fdfcc160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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keDRVJsLDw8369et9tfn8wxj47W17N1FKoh2repttRVH11jxXDBwOB48++iiT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\nIitEpGYO1tFFRGqne/+KiNyRxXcWiUi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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c5fecc7978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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lqOHlxi8j21PKqeg/ZplvtyPg2/tN8xekV66maQBa789NScDJDfw62kVCAGja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\n5c5gMPDUU0+xePFi7rvvPvbv38/w4cN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u64CnC/Tj2pGKK3PhKkJjvDCmdQDejnPoTYQS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\n2lT/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c5febdf978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FlClTxu5YPkWLglLukHzS6kf053sXB7TRBnV51qpVKypUqMDcuXPp16+f3XF8khYFpQri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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c5fedf5630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Wo0eP5tixYwwaNMjusJRSZfDmieZkEbnC8kicSgeZS1VUwG7AgAG0aNGCr776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\ntSvR0dGWDzRjjLHkC7gZeNt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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c5fedf2b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gx6qg+m3MmDF07dqVzMxM+vfvz+HDh+nevbvbYRljiuHPjOZNInKe45EESYUX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\nLM9HjTGmSLaeQtmsA5qJSFM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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c5febd6c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0acPKlSvp3LmISrbKrxSZFIwx64H1IjLHGJPuxZgC2uxle53tvq1rE1O1HBS9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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c5fedb0c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zKFtp50eqMPFzGKSlpdG7d2/Wr1/Pli1b6NKlC8eOHfP5dpRzvAmF9saYZ4Bn0meIyN1Y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\nWLNmDR07dmT//v0UK1aM999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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c5fee537b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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REoLxS1EthYtF5Gx5bAFa+iyjqjcVd3ARiQVeASoBs1R1agH79AL+AlQBDqnqtf6Hb8qd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\nYIwxJo8lBWOMMXksKRhjjMnz/wFfSGgL7fGUngAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c5fec33c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import label_binarize\n",
    "\n",
    "#感知机没有 概率 \n",
    "draw_roc(y_train,y_train_pt,'pt_train')\n",
    "draw_roc(y_test,y_test_pt,'pt_test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "draw_roc(y_train,y_train_pro_lr[:,1],'lr_train')\n",
    "draw_roc(y_test,y_test_pro_lr[:,1],'lr_test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "draw_roc(y_train,y_train_pro_svm[:,1],'svm_train')\n",
    "draw_roc(y_test,y_test_pro_svm[:,1],'svm_test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "draw_roc(y_train,y_train_pro_dt[:,1],'dt_train')\n",
    "draw_roc(y_test,y_test_pro_dt[:,1],'dt_test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "draw_roc(y_train,y_train_pro_gnb[:,1],'gnb_train')\n",
    "draw_roc(y_test,y_test_pro_gnb[:,1],'gnb_test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "draw_roc(y_train,y_train_pro_rf[:,1],'rf_train')\n",
    "draw_roc(y_test,y_test_pro_rf[:,1],'rf_test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "draw_roc(y_train,y_train_pro_gbdt[:,1],'gbdt_train')\n",
    "draw_roc(y_test,y_test_pro_gbdt[:,1],'gbdt_test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "draw_roc(y_train,y_train_pro_bg[:,1],'bg_train')\n",
    "draw_roc(y_test,y_test_pro_bg[:,1],'bg_test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "draw_roc(y_train,y_train_pro_ab[:,1],'ab_train')\n",
    "draw_roc(y_test,y_test_pro_ab[:,1],'ab_test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "draw_roc(y_train,y_train_pro_xgb[:,1],'xgb_train')\n",
    "draw_roc(y_test,y_test_pro_xgb[:,1],'xgb_test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "draw_roc(y_train,y_train_pro_lgbm[:,1],'lgbm_train')\n",
    "draw_roc(y_test,y_test_pro_lgbm[:,1],'lgbm_test')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    " #画P-R曲线\n",
    "    \n",
    "#  用P-R曲线判定分类器效果，方法很简单，就看曲线和XY轴围成图形的面积，面积越大，分类器效果越好。 \n",
    "from sklearn.metrics import precision_recall_curve\n",
    "def draw_pr(y_true, y_pred,label=None):\n",
    "    precision,recall,thresholds = precision_recall_curve(y_true, y_pred)\n",
    "    print(precision,'\\n',recall,'\\n',thresholds)\n",
    "    plt.plot(recall,precision,linewidth = 2, label=label)\n",
    "    plt.plot([0,1],[0,1], 'k--')\n",
    "    plt.axis([-0.05,1.05,-0.05,1.05])\n",
    "    plt.xlabel(\"Recall Rate\")\n",
    "    plt.ylabel(\"Precision Rate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.25965131  0.25942074  0.25950156 ...,  1.          1.          1.        ] \n",
      " [ 1.          0.99880096  0.99880096 ...,  0.00239808  0.00119904  0.        ] \n",
      " [ 0.0235701   0.02362303  0.02364767 ...,  0.99963826  0.9998569\n",
      "  0.99989261]\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZFrDLbZJTUvnfxNWsP3A63f47apaga8OyVC8ZTNUSwTqc1SIu6ymISCvga2A/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c5fed02390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_pr(y_train,y_train_pro_lr[:,1],'lr_train')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FLUThvPXWW0ybNo22bduye/duo8Mp8fK7eE1WNJcUMSGw6Fm4chbcG8HQVVCxVo7TDkZe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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c5834e5470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JEs+USeEo0Bw4pLVurpSqCPykte6Zy3nWQBDQEwgD9gPDtNbH07VxBXYDPlrr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\nIYRQEkUhhBBCyT/tMJCI7RMy8AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c5febb0a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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l2AkKCsLJyQk3NzfmzJmDEIJRo0aZOqwiY3Z/H1pWc+XtVScz943/4xS7xncw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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c5fed7e278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PPMOkSZPsTqhUgeTMheZvAZ9Mj0sC65zZuIiEiMh+EQkXkReusl5LEUkTkd7O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\nTZs2dO7cGYD+/ftrQVDKjYw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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c5ea805b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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h12fQqAcMeROqVIWIrXB6J+z85ErbPo/BvwWUlfIfDrd/CQ6O5rkWAcCeiHjG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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c58350c390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ljPGOmDZy6q1HlaoxrZKqlFKqgvYUlFJKVdCkoJRSqoIm\nBaWUUhU0KSillKqgSUEppVQFTQpKKaUqaFJQSilVQZOCUkqpCv8PnLbYAWfZn2cAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c583528400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HugCXpJQjgAcA18IuEkJYA18DQWhvGYOEEI3vOMcNmAP0kVI2AZ64t/AVs2n2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\nYGZmBRcFMzMr/A/PGeeHAp2jNQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c5835197f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ATSHEr8Am4G1TBqWUstvzFqo2hCFroe9c6P9rzvGECC1h3IyCAwu1iXBKuffG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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c5fec5eda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AOKAicIPMpBALLJVSLtBTrAWikoKJCvwT/vdIHSYzC9ClagsFvZLHaCZFb46F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\nilwUzMysyEXBzMyK/ge7eNjiJUkuQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c5834e1dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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b3UkKszi7uqlSRV/F2nDVw7Yd2GZXUSfEuRbNxdkWGAJX3GgvMdXrBCFhLFu2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\nxhtvUKpUKT777DOGDh3qqY9TSvmRvPs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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c583538e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gUvnB008/Tfv27Tl69Ch9+vQhKSmpcCQEsKrKPp8CgxZlrjtzDEpVunA5ZjGc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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c5fec5e860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#感知机没有 概率 \n",
    "draw_pr(y_train,y_train_pt,'pt_train')\n",
    "draw_pr(y_test,y_test_pt,'pt_test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "draw_pr(y_train,y_train_pro_lr[:,1],'lr_train')\n",
    "draw_pr(y_test,y_test_pro_lr[:,1],'lr_test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "draw_pr(y_train,y_train_pro_svm[:,1],'svm_train')\n",
    "draw_pr(y_test,y_test_pro_svm[:,1],'svm_test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "draw_pr(y_train,y_train_pro_dt[:,1],'dt_train')\n",
    "draw_pr(y_test,y_test_pro_dt[:,1],'dt_test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "draw_pr(y_train,y_train_pro_gnb[:,1],'gnb_train')\n",
    "draw_pr(y_test,y_test_pro_gnb[:,1],'gnb_test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "draw_pr(y_train,y_train_pro_rf[:,1],'rf_train')\n",
    "draw_pr(y_test,y_test_pro_rf[:,1],'rf_test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "draw_pr(y_train,y_train_pro_gbdt[:,1],'gbdt_train')\n",
    "draw_pr(y_test,y_test_pro_gbdt[:,1],'gbdt_test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "draw_pr(y_train,y_train_pro_bg[:,1],'bg_train')\n",
    "draw_pr(y_test,y_test_pro_bg[:,1],'bg_test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "draw_pr(y_train,y_train_pro_ab[:,1],'ab_train')\n",
    "draw_pr(y_test,y_test_pro_ab[:,1],'ab_test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "draw_pr(y_train,y_train_pro_xgb[:,1],'xgb_train')\n",
    "draw_pr(y_test,y_test_pro_xgb[:,1],'xgb_test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "draw_pr(y_train,y_train_pro_lgbm[:,1],'lgbm_train')\n",
    "draw_pr(y_test,y_test_pro_lgbm[:,1],'lgbm_test')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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